基于改进PSO算法和LS-SVM的短期电力负荷预测

针对电力负荷的小样本、非线性、高维数和局部极小点等问题,提出采用最小二乘支持向量机方法建模,以历史负荷、温度、湿度等数据作为输入量,对短期电力负荷进行预测;针对最小二乘支持向量机在建模中存在的参数选取问题,采用一种根据种群多样性信息来指导初始种群选取和避免粒子早熟收敛现象的改进粒子群优化算法来优化最小二乘支持向量机的惩罚因子和核参数。仿真结果表明,基于改进粒子群优化算法和最小二乘支持向量机的短期电力负荷预测方法较最小二乘支持向量机预测方法、基于基本粒子群优化算法和最小二乘向量机的预测方法具有更好的预测精确度。

Forecasting of Short-term Power Load Based on Improved PSO Algorithm and LS-SVM

For problems of small samples,nonlinear,high dimensions and the local minimum of electric power load,a modeling method based on the least square support vector machine was proposed to forecast short-term power load by taking historical load,temperature and humidity data as inputs.For parameter selection problem of the least square support vector machine in modeling,an improved particle swarm optimization algorithm was used to optimize two parameters of penalty factor and kernel function of the least square support vector machine model,which guides initial population selection and avoids premature convergence of the particle according to diverse information of population.The simulation results showed that the method based on the improved particle swarm optimization algorithm and the least square support vector machine model has higher accuracy for predicting short-term load in comparison with method of the least square support vector machine model and method of the particle swarm optimization algorithm and the least square support vector machine model.